1. Field of the Invention
The present invention relates to a cognitive architecture for production and laser processing agents, in particular to a method for closed-loop controlling a laser processing operation and to a laser material processing head using the same.
2. Description of Related Art
In industrial nations with high labor and living costs, it is important to increase automation in manufacturing in order to retain a competitive edge. Furthermore, there is an ongoing trend from mass production toward increased flexibility in product variation while maintaining high output volumes. Cognitive capabilities for production machines may improve with flexibility and automation to contribute to a Cognitive Factory of the future. Artificial software agents, systems with cognitive capabilities, subsequently just agents, may help to tailor products individually and to deliver them on a large scale. Furthermore, due to a computer's superior skills in data analysis, agents may be able to manage complex production tasks that are challenging even for human experts. A possible test scenario for these agents could be an upcoming production method that is complex to handle and needs improvement in terms of flexibility. Therefore laser material processing may be a good choice for investigating the cognitive capabilities of artificial agents in performing production tasks.
Treating materials with laser beams has several advantages. The laser is one of the highest density energy sources available to industry. Once configured, a laser processing system works with extraordinary precision, enabling high cut quality in laser cutting or deep and thin weld seams in laser beam welding. Therefore laser material processing is already frequently applied in a great variety of production processes, mostly out of the public view. Automotive manufacturers apply laser processing in many steps of car body production, but laser processing is also used for consumer and medical products such as household devices or coronary stents. However, users must expend a great deal of cost and effort on trials before a laser processing system can run. For every desired change in the processing task, the user may have to repeat the configuration procedure. Even if all process parameters remain untouched, slight differences in workpiece, workload, alignment, room temperature, or optical properties may result in a loss of quality and, in the worst case, a suspension of the assembly line. Laser cutting and laser welding may thus benefit from the cognitive capabilities of artificial agents. If these agents can learn how to weld or cut, it would not only reduce the system configuration effort, but also increase its flexibility. Moreover, if an agent could improve itself over time, it could gain the capability to develop its everyday tasks, increase output, and assure quality. Many manufacturers wish to have a prompt cutting or welding technique, a system that does not need to be reconfigured when it takes over a new production task. This kind of system would significantly increase welding and cutting efficiency and assure quality. Quality assurance is especially important when processing parts are associated with safety, for instance within cars or airplanes.
Another advantage of process control, besides increased quality and flexibility, would be to save environmental resources. For instance laser cutters use higher laser power than necessary as a safety margin to maintain a minimum kerf width and to prevent a loss of cut. Artificial software agents might learn to apply just enough energy for optimal cutting efficiency, thus saving energy with every cut. For example, five 8 kW fiber lasers with a wall plug efficiency of 30% integrated in an assembly line in Germany operating 253 days a year, 18 hours a day, create operational electricity costs of over US$50,000 annually. With 32 g CO2 emission per kWh, this adds up to approximately 20 metric tons annually. In addition, fiber lasers apparently have a better wall plug efficiency compared to other common industrial laser sources, which are sometimes less efficient by a factor of 15. Taking only a factor of six, this would add up to operational electricity costs of US$300,000 and over 100 metric tons of annual CO2 emission. If an artificial agent manages to save just 10% in laser power, this may save up to US$30,000 and approximately 2 to 10 or more metric tons of annual CO2 emission within just one sample assembly line. It however remains unclear whether it is possible to define a cognitive architecture that can create artificial agents that can learn tasks from laser cutting or welding and can then reliably monitor and control in real-time, improve processing, and save resources.
State-of-the-Art Scientific and Industrial Approaches
In general, laser material processes are established and configured through a series of trials. Reference tests are carried out until a human expert has found a possible parameter set. In welding, the weld is analyzed with microscopic pictures of a cross-section of the seam. Finally, once the user finds successful parameter sets, the parameters remain untouched and any process disturbance is excluded if possible. Because this process involves high effort and cost, manufacturers often declare the parameter sets to be classified. However, even if every attempt is made to keep all process parameters constant, slight changes and nonlinear behavior can result in poor cutting quality or welding defects. For quality assurance, many industrial users need to implement monitoring systems to observe their laser processes.
Monitoring in Laser Material Processing
There are two general monitoring standards in laser cutting: maintaining a minimum kerf width and a certain cutting edge quality. Problems in cutting edge quality include, for instance, dross, roughness, or parallelism of edges. The overall quality or variation in edge roughness is determined by many parameters such as room environment, gas and nozzle parameters, focus position, laser power, feed rate, angle or radius parameters, laser beam conditions and alignment, the metal alloy, surface coatings, among many others. A welding seam may have undesired surface irregularities, including breaks, holes, material ejections, the formation of spatters, cracks, pores, seam width variation, and many more. Sophisticated monitoring systems have thus been introduced for industrial laser welding to detect the problems listed; there are three types: pre-, in-, and post-process monitoring. A number of publications have emphasized that in-process or online process monitoring may detect welding defects. On top of these, there may also be welding errors, such as an undesired degree of welding depth or insufficient connection, which often cannot be observed without destroying the welding seam. The latter may expand and lead to a complete lack of fusion. A lack of fusion involves a gap between the partners that should have been joined. The gap is often visible neither from the top nor from the bottom of the welded workpiece and is therefore called a false friend.
A frequently used sensor for monitoring laser cutting as well as welding is a camera aligned coaxially to the laser beam axis. Such a camera can capture images of the heat affected zone and the treated workpiece. It may also be suitable for closed-loop process control. Related research indicates that a coaxially observing camera can allow monitoring of the appearance of dross and the existence of an insufficient cut or minimum kerf width. An illumination unit may significantly improve monitoring with cameras because the workpiece surface and more details of the heat affected zone are visible. The coaxially integrated camera is a very valuable sensor for monitoring cutting and welding processes and providing temporally and spatially resolved process information. As mentioned above, detecting false friends is a difficult monitoring task because the weld seam may appear defect-free from the outside at the location of a lack of fusion. The defect may, for example, be monitored with thermal imaging. When observing a thermal image of an integrated camera, the heat dissipation seems to be longer towards the cooling weld seam if a false friend is present. A temperature ratio between two positions of the cooling weld seam, one closer to the focal spot than the other, may detect the presence of a lack of fusion in some cases. On the contrary, this method seems to require costly equipment and the configuration of fixed measurement positions. This method has to be however manually adapted to the individual processes.
In an analysis of the optical spectrum of process emission in laser welding or cutting, differences in distribution and intensity coincide with process changes. The same seems to be true for air-borne or solid-borne acoustic emissions. Optical as well as acoustical emissions seem to deliver similar process information. Wavelength filtered photodiodes often capture information on specific spectral process emissions. Many users thus apply three photodiodes, respectively sensitive to laser back reflection, temperature, and plasma plume emissions.
However, it is hardly possible to cover all of the effects in laser material processing with just one kind of sensor technology. Thus, combining several sensor signals for improved monitoring of laser material processing has several advantages.
Closed-Loop Control in Laser Material Processing
The vast majority of industrial laser material processing applications are manually configured and supervised. It is economically worthwhile to decrease human labor costs and system downtime for laser processing systems. As a result, it has been a long-term research goal to achieve closed-loop control of at least one influential process parameter. Some parameters of laser material processing have a short response time and a great influence on the process outcome. Therefore, these parameters have been subdivided into two groups: distance control and laser power control.
As stated above, receiving a failure-free monitoring signal in laser material processes is challenging in both laser welding and laser cutting. Nevertheless, many theories have been developed and some specialized systems are now used in industrial environments, such as capacitive distance control in laser cutting. The capacitive distance control works reliably in many industrial applications to maintain a constant distance between the workpiece and the processing head.
Some attempts have been made to attain closed-loop laser power control; for example, a laser power is controlled by a threshold function for a process emission photodiode. This method made it possible to find a fixed relation between weld speed and laser power. Photodiode signals may vary significantly with slight process parameter changes. Therefore control methods with static functions of photodiode intensity to laser power suffer from process disturbances. A photodiode mounted at the bottom of the welded workpiece detects different intensities depending on the degree of workpiece penetration. These root side light emissions control the laser power within a closed loop. For many industrial applications, this method is not suitable because the root side of the workpiece is not accessible. Furthermore, this method only works for full penetration welds when the laser beam exits the work-piece at the root side. Closed-loop control of laser power and focal position has also been studied. In this case, a fixed threshold for keyhole opening at a fixed position that controls the laser power and the focal position is altered with changes in chromatic aberration. The keyhole opening seems to be a significant camera picture feature suitable for full penetration welding. However, many welding processes do not have a visible keyhole within the camera image. Often the keyhole is only visible in full penetration welding with high laser power, resulting in significant heat conduction within the workpiece and excessive penetration with weld seam root convexity.
Besides using a processing head that works relatively close to the workpiece, it is possible to use so-called scanners with beam guiding mirrors for remote welding applications. Monitoring systems for remote welding is a promising topic for future research. An approach for laser power control within remote welding has been demonstrated with sophisticated experimental results. An algorithm finds a keyhole within a camera equipped with a Cellular Neural Network environment. A control loop increases the laser power until a keyhole is detected within the camera picture and maintains it at a constant size. However, as stated above, a keyhole is only visible within the camera picture when there is very high laser power resulting in significant heat influence on the workpiece. Furthermore, only full penetration welding is possible with this technique, but is not desired in every case.
Closed-loop control seems to be a highly complex task for laser material processing. Most monitoring signals merely give relative feedback rather than absolute values. Small changes in the distance between the workpiece and the processing head may result in different absolute values for monitoring signals, but with the same process result. The proposed approaches for closed-loop control seem to be suitable only for defined process modes such as full penetration welding with a high level of laser power or fixed thresholds. A possible cure for a closed-loop control system would add increased adaptability, as will be discussed in the next section.
Adaptive Control and Monitoring Approaches in Laser Material Processing
With the many quality control and closed-loop control systems that have been explored in the literature, there must be some reason why only a few are applicable for industrial use. One reason may be that these systems only work for individual applications but are not suitable to cover a great variety of different processes. An enhanced adaptability may be a solution to this problem. If a system can learn how to adapt to a certain number of distinct applications, this may already be more valuable for manufacturing purposes. Moreover, it appears that an ideal sensor that always gives accurate and absolute information about the processing state has not been found for laser material processing. An evaluation of multiple sensor data input may help to improve the monitoring results and better to grasp the system's state. In this way, many sensor data inputs with individual weaknesses may be combined to become more reliable, in the same way that humans rely on several senses to make judgments. Thus cognitive capabilities may help to bridge the existing gap and apply laser material processing to more manufacturing processes, increase quality performance, and decrease wastage of resources.
Several sophisticated approaches using methods from machine learning or with cognitive capabilities have already been discussed in the literature. The general idea of an autonomous production cell for laser beam welding has been investigated. Other approaches may be subdivided into systems that combine one or more sensors intelligently to monitor the process, and approaches that aim to control the process.
Recent techniques in machine learning and the control of laser beam welding have been examined to create adaptive monitoring. Artificial Neural Networks (ANN), Support Vector Machines (SVM), and the Fuzzy K-Nearest Neighbor (KNN) classification have been investigated as they apply to special applications for laser material processing.
In order to control the welding speed, a method of defining thresholds with fuzzy logic rules has been provided. This is studied in combination with a fuzzy logic process control. Here, the process information is first analyzed statistically before it is used for closed-loop control to cope with the fact that the information gained about the process is weak for closed-loop controlling purposes. Related work using an expert system can be found. ANN for laser material processing purposes have been investigated. An ANN is investigated to create a predictive process model of optical process emission, welding speed, laser power, and focal position, which is then adapted to the process. This is a promising approach, but the necessity of first defining a process model creates additional effort. One aim of the present invention is to evaluate what machine learning can accomplish without a process model.
Although there has been significant scientific interest in finding an adaptive system that can manage different tasks in laser material processing, it seems as if this step still needs to be taken. Either the discussed approaches do not include experimental data or they seem to be suitable only for specific applications.
In summary, laser material processing systems require a major effort in installation and reconfiguration. Typically, the systems are set up to execute a specific task in the same way again and again. The current aim for these systems is to keep all of the influential parameters constant, which is often not the case in real industrial applications. Materials vary from piece to piece or from one workload to the next. The mounting may not be the same all the time because of variations resulting from either human labor or imprecise robots. However, there is a great desire for fault-free weld seams and stable cutting quality. This results not only from a need to optimize manufacturing economically or to conserve environmental resources, but also because this is a major safety issue, especially for car or airplane bodies. This means that quality control is essential, along with, ideally, closed-loop process control systems that are able to work reliably in the demanding environment of material processing with high-powered laser beams. It seems as if these goals have not been met by the current state of research, as is described above.